Artificial Intelligence and Machine Learning in Crystallography Editorial for Crystallography Reviews, Issue 2 of Volume27, 2021

“Everythingwe love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as wemanage to keep the technology beneficial.” saidMax Tegmark, President of the Future of Life Institute [1]. There is half a century of evolution behind artificial intelligence (AI) and machine learning (ML). The exponentially developing technology can do a good job at narrow tasks for example in mathematics, modelling climate change, internet searches, facial recognition, speech recognition, driving autonomous cars, customer service, playing chess, or Facebook uses algorithms to block content that breaks its rules. It can be applied in automated stock trading, it is offered for the commercial sectors, solving business problems for public and private sectors. Science fiction often portrays artificial intelligence with human-like characteristics, which emerges conversations about the impact on society and around the ethics of AI. Artificial General or Super Intelligence is a theoretical form of AI, where it would have a self-aware consciousness that had the ability to solve problems surpassing the intelligence and capacity of the human brain. An example isHAL, the rogue computer assistant in 2001: A Space Odyssey. Back to reality, algorithms cannot understand the essence of humans: emotion, morality, culture, since these abilities cannot be expressed in mathematical equations. Artificial Intelligence enables problem-solving by the combination of computer science and robust datasets. Machine learning is the subfields of artificial intelligence. Deep learning refers to a neural network, which comprise of multiple hidden layers between the input and output layers. Machine learning and deep learning differs in the way how their algorithms learn.Machine learning ismore dependent on human intervention, what determines the hierarchy of features. A deep learning-based systemwould be able to achieve the same task in a much shorter time. MelanieVollmar andGwyndaf Evans from theDiamondLight Source Ltd., and from the Rosalind Franklin Institute, respectively, both in Harwell Science and Innovation Campus, Didcot, UK, review the “Machine learning applications in macromolecular X-ray crystallography” in Issue 2 of Volume 27 of Crystallography Reviews. We can quickly understand why introducing Artificial Intelligence is somuch investigated and needed at the Diamond Light Source reading the facts: throughout 2020 user access to MX beamlines was almost exclusively remote, in the 11 months from June 2020 over 33,000 data sets were measured, typically less than 3minutes being used for each crystal sample, the yearly quantities of measured data reach many Petabyte. We may learn from the article that AI can contribute to the question of crystallisability, to the detection of the presence of crystals in crystallisation trials, to forecast of experimental data and data analysis outcome, to have real-time